2023/FinetuningLargeLanguageModels/03_Instruction_tuning_lab_student.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKn-Y_Pk9WjC"
   },
   "source": [
    "# Instruction-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YwB8OLqiflAl"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import jsonlines\n",
    "\n",
    "from datasets import load_dataset\n",
    "from pprint import pprint\n",
    "\n",
    "from llama import BasicModelRunner\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load instruction tuned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruction_tuned_dataset = load_dataset(\"tatsu-lab/alpaca\", split=\"train\", streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 5\n",
    "print(\"Instruction-tuned dataset:\")\n",
    "top_m = list(itertools.islice(instruction_tuned_dataset, m))\n",
    "for j in top_m:\n",
    "  print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two prompt templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt_template_with_input = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Input:\n",
    "{input}\n",
    "\n",
    "### Response:\"\"\"\n",
    "\n",
    "prompt_template_without_input = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{instruction}\n",
    "\n",
    "### Response:\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrate prompts (add data to prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_data = []\n",
    "for j in top_m:\n",
    "  if not j[\"input\"]:\n",
    "    processed_prompt = prompt_template_without_input.format(instruction=j[\"instruction\"])\n",
    "  else:\n",
    "    processed_prompt = prompt_template_with_input.format(instruction=j[\"instruction\"], input=j[\"input\"])\n",
    "\n",
    "  processed_data.append({\"input\": processed_prompt, \"output\": j[\"output\"]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(processed_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data to jsonl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with jsonlines.open(f'alpaca_processed.jsonl', 'w') as writer:\n",
    "    writer.write_all(processed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare non-instruction-tuned vs. instruction-tuned models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path_hf = \"lamini/alpaca\"\n",
    "dataset_hf = load_dataset(dataset_path_hf)\n",
    "print(dataset_hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-hf\")\n",
    "non_instruct_output = non_instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Not instruction-tuned output (Llama 2 Base):\", non_instruct_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruct_model = BasicModelRunner(\"meta-llama/Llama-2-7b-chat-hf\")\n",
    "instruct_output = instruct_model(\"Tell me how to train my dog to sit\")\n",
    "print(\"Instruction-tuned output (Llama 2): \", instruct_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chatgpt = BasicModelRunner(\"chat-gpt\")\n",
    "instruct_output_chatgpt = chatgpt(\"Tell me how to train my dog to sit\")\n",
    "print(\"Instruction-tuned output (ChatGPT): \", instruct_output_chatgpt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try smaller models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/pythia-70m\")\n",
    "model = AutoModelForCausalLM.from_pretrained(\"EleutherAI/pythia-70m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=100):\n",
    "  # Tokenize\n",
    "  input_ids = tokenizer.encode(\n",
    "          text,\n",
    "          return_tensors=\"pt\",\n",
    "          truncation=True,\n",
    "          max_length=max_input_tokens\n",
    "  )\n",
    "\n",
    "  # Generate\n",
    "  device = model.device\n",
    "  generated_tokens_with_prompt = model.generate(\n",
    "    input_ids=input_ids.to(device),\n",
    "    max_length=max_output_tokens\n",
    "  )\n",
    "\n",
    "  # Decode\n",
    "  generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)\n",
    "\n",
    "  # Strip the prompt\n",
    "  generated_text_answer = generated_text_with_prompt[0][len(text):]\n",
    "\n",
    "  return generated_text_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "finetuning_dataset_path = \"lamini/lamini_docs\"\n",
    "finetuning_dataset = load_dataset(finetuning_dataset_path)\n",
    "print(finetuning_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sample = finetuning_dataset[\"test\"][0]\n",
    "print(test_sample)\n",
    "\n",
    "print(inference(test_sample[\"question\"], model, tokenizer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare to finetuned small model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "instruction_model = AutoModelForCausalLM.from_pretrained(\"lamini/lamini_docs_finetuned\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(inference(test_sample[\"question\"], instruction_model, tokenizer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mSJMi8I4Sgrw"
   },
   "outputs": [],
   "source": [
    "# Pssst! If you were curious how to upload your own dataset to Huggingface\n",
    "# Here is how we did it\n",
    "\n",
    "# !pip install huggingface_hub\n",
    "# !huggingface-cli login\n",
    "\n",
    "# import pandas as pd\n",
    "# import datasets\n",
    "# from datasets import Dataset\n",
    "\n",
    "# finetuning_dataset = Dataset.from_pandas(pd.DataFrame(data=finetuning_dataset))\n",
    "# finetuning_dataset.push_to_hub(dataset_path_hf)"
   ]
  }
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